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I'm doing a paper for a class on the topic of big problems that are still prevalent in AI, specifically in the area of natural language processing and understanding. From what I understand, the areas:

  • Text classification
  • Entity recognition
  • Translation
  • POS tagging

are for the most part solved or perform at a high level currently, but areas such as:

  • Text summarization
  • Conversational systems
  • Contextual systems (relying on the previous context that will impact current prediction)

are still relatively unsolved or are a big area of research (although this could very well change soon with the releases of big transformer models from what I've read).

For people who have experience in the field, what are areas that are still big challenges in NLP and NLU? Why are these areas (doesn't have to be ones I've listed) so tough to figure out?

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According to a nice article by Sebastian Ruder https://ruder.io/4-biggest-open-problems-in-nlp/ based on answers from top NLP researchers https://docs.google.com/document/d/18NoNdArdzDLJFQGBMVMsQ-iLOowP1XXDaSVRmYN0IyM/edit

  1. Natural language understanding
  2. NLP for low-resource scenarios
  3. Reasoning about large or multiple documents
  4. Datasets, problems, and evaluation

I recommend having a look at the article. More details in the slides https://drive.google.com/file/d/15ehMIJ7wY9A7RSmyJPNmrBMuC7se0PMP/view

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According to Dan Jurafsky, a researcher on NLP and NLU, the current hard problems in NLP are (see slide 6)

  • Questioning answering
  • Paraphrase
  • Summarisation
  • Dialogue

Other hard problems for which there are already some good solutions are

  • Sentiment analysis
  • Coreference resolution
  • Word sense disambiguation
  • Parsing
  • Machine translation
  • Information extraction

Ambiguity in natural language is one of the biggest challenges for NLP and NLU systems. Other challenges are (see slide 10)

  • non-standard words
  • idioms
  • tricky entity names
  • neologisms
  • world knowledge
  • segmentation issues
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